Recording videos under dusty and foggy conditions presents numerous challenges due to frequently low light levels, reduced brightness, contrast, and increased noise, all of which significantly degrade the overall image quality. Such adverse weather conditions profoundly impact daily life by deteriorating air quality, reducing visibility, and causing health hazards like respiratory problems and asthma. Furthermore, these conditions disrupt travel by causing accidents, flight delays, and challenges for marine and rail transportation systems. “Automated Surveillance Video Visibility Enhancement Based on Transfer Learning Using an Unsupervised Learning Approach” aims to enhance the clarity of video frames. This model utilizes many unsupervised learning methods, such as multispectral video frame enhancement and fog rectification techniques, to improve image sharpness, contrast, and brightness, successfully reducing the interference of dust and fog-induced noise and distortions. This model system provides a comprehensive answer to the issue of reduced video quality in unfavorable weather conditions. Our proposed system shows very promising results when it comes to the calibration results. Different evaluation metrics have been implemented, such as SNR, PSNR, and MSE, to test our system. The experimental results indicate that the suggested approach surpasses conventional video enhancement techniques and cutting-edge machine learning methods in terms of accuracy and efficiency. The improved video quality enables more effective monitoring and analysis, essential for applications such as ensuring transportation safety, monitoring the environment, and conducting public health surveillance. This work has consequences that go beyond only video surveillance. It offers useful ideas and approaches that may be used in several fields where improving visibility is crucial.